Abstract

Determination of the lag length of an autoregressive process is one of the most difficult
parts of ARIMA modeling. Various lag length selection criteria (Akaike Information
Criterion, Schwarz Information Criterion, Hannan-Quinn Criterion, Final Prediction
Error, Corrected version of AIC) have been proposed in the literature to overcome this
difficulty. We have compared these criteria for lag length selection for three different
cases that is under normal errors, under non-normal errors and under structural break by
using Monte Carlo simulation. It has been found that SIC is the best for large samples
and no criteria is useful for selecting true lag length in presence of regime shifts or
shocks to the system.

Item Type:

MPRA Paper

Original Title:

Performance of lag length selection criteria in three different situations